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Sökning: WFRF:(Liang Yujian)

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  • Wang, Ning, et al. (författare)
  • Boride-derived oxygen-evolution catalysts
  • 2021
  • Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 12
  • Tidskriftsartikel (refereegranskat)abstract
    • Metal borides/borates have been considered promising as oxygen evolution reaction catalysts; however, to date, there is a dearth of evidence of long-term stability at practical current densities. Here we report a phase composition modulation approach to fabricate effective borides/borates-based catalysts. We find that metal borides in-situ formed metal borates are responsible for their high activity. This knowledge prompts us to synthesize NiFe-Boride, and to use it as a templating precursor to form an active NiFe-Borate catalyst. This boride-derived oxide catalyzes oxygen evolution with an overpotential of 167 mV at 10 mA/cm2 in 1 M KOH electrolyte and requires a record-low overpotential of 460 mV to maintain water splitting performance for over 400 h at current density of 1 A/cm2. We couple the catalyst with CO reduction in an alkaline membrane electrode assembly electrolyser, reporting stable C2H4 electrosynthesis at current density 200 mA/cm2 for over 80 h.
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  • Xu, Yu, et al. (författare)
  • An atlas of genetic scores to predict multi-omic traits
  • 2023
  • Ingår i: Nature. - : Springer Nature. - 0028-0836 .- 1476-4687. ; 616:7955, s. 123-131
  • Tidskriftsartikel (refereegranskat)abstract
    • The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.
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